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8/12｜Creative Montage：展示剪辑和编码能力的混剪 素材丰富、不重复镜头，英文呈现，声音一直照顾到结尾，把重点放在后段更强的高潮。 需要：可用的视频/图片素材、剪辑合成和音频。Opus / Sonnet 可编排镜头、转场与代码动效；若素材不够，再考虑接 Seedance 等工具补镜头，原 Prompt 没有强制要求生成视频。 完整 Prompt： Create an impressive montage that shows off your editing and coding abilities. Use rich, varied material without repeating shots, with polished English presentation and sound through the ending. Focus the edit on the stronger climax in the later part.

here's a prompt to improve your agent harness based on what we've learned at cursor. enjoy # Improve this agent harness's token efficiency You're working on an LLM agent harness: the system prompt, tool definitions, request assembly, context caching, compaction, and retrieval, and how work is split across agents. Make the agent's runs cheaper without making it worse at its job. - Objective: lower price-weighted token cost per completed task. - Constraint: no measurable drop in task quality. Measure per task, not per request. Every turn resends the prefix (tools, instructions, setup, and the conversation so far), so a change that shrinks each request but adds turns can cost more. Weight tokens by billing type: output, uncached input, and cached input are priced very differently. Work in this order: map the harness and measure the baseline, rank the opportunities, make the changes that are safe to make directly, put the rest behind flags or in proposals, then report. Figures below come from one team's production coding agent and its multi-agent experiments. Use them to gauge magnitude, not as targets. One round of these changes (prompt trimming, tool offloading, cache layout, sparse line numbers, subagent tuning) cut that team's overall token cost about 7% with no loss in quality. The larger percentages apply only to the part of the request each change touched. ## Principles 1. Change what the harness sends, not how hard the model tries. Don't ask the model to conserve tokens. A harness that told its model to "take care to preserve tokens and not be wasteful" found it grew reluctant to take on ambitious tasks and sometimes quit, saying it wasn't supposed to waste tokens. 2. Capable models need definitions, not commands. Lists of "DO NOT", "You must", and "Important", and guards against older models' habits, can usually be replaced with plain descriptions of what each tool does. One team cut about two-thirds of its system prompt this way, and the shorter prompt worked across model families. Instruct only on what the model can't know (the product, the environment, the user's processes) and on quirks you've seen in transcripts. 3. Static context is for what most turns need. Everything else should be discoverable when needed. Less up-front context also means less confusing or contradictory information. 4. Expect removals to win. Guardrails written for weaker models, coordination steps that became bottlenecks, and prompting for behavior the model now does on its own all cost tokens. 5. Real usage decides. Evals are a fast proxy, but they skew toward hard problems and miss the real mix of requests. ## 1. Map the harness and measure the baseline Find: - Where requests are assembled, the system prompt, and tool schemas. If a framework or SDK builds requests, find its hooks for message order, cache control, and tool loading. - How tool results are formatted, and how history is kept, trimmed, or summarized. - How subagents or parallel agents are spawned, if any. - Which models and provider APIs are used. From the provider's docs, get the prompt caching behavior (automatic or explicit breakpoints, TTL, minimum cacheable length) and the prices for output, uncached input, and cached input. - Existing logging, token accounting, and evals. If the harness doesn't record per-request token usage by billing type and cache hits, add that first. Everything later depends on it. Then render a few real requests (from logs, or by running representative tasks) and count tokens per section with the model's tokenizer or the API's usage fields. Produce: - Cost share by source × billing type. Sources: system prompt, tool definitions, skill/rule/integration descriptions, user messages, file reads, search results, command and other tool output, history, summaries, subagents. - Static tokens per request, cache hit rate, and turns per task. - Per tool: the share of runs that call it at least once, and its error rate. Read the rendered requests, not just the templates. Duplication, leaked volatile values, and misordered blocks only show up there. Rank opportunities by share of spend × fraction removable ÷ quality risk. ## 2. System prompt and injected context Label every instruction: - Keep: product or environment knowledge the model can't infer, fixes for quirks seen in this model's transcripts, and rules a mode depends on. - Rewrite: commands and emphasis into plain descriptions. Reminders into constraints: "No TODOs, no partial implementations" works better than "remember to finish implementations." Vague quantities into ranges: "generate 20–100 tasks" gets far more ambitious behavior than "generate many tasks." - Delete: things capable models do by default, guards against behavior you haven't seen from this model, text that repeats tool descriptions, and lines that could contradict a user request. Models trained to rank system instructions above user messages will side with the system prompt. - Move: anything per-user or per-request (date, environment, repo state, lists of skills or subagents, user rules) into a user-role setup message after the cache boundary. Audit other injected context the same way. As models improved, the team behind these figures dropped directory trees, pre-retrieved snippets, compressed copies of attached files, lint errors injected after every edit, forced expansion of short file reads, and caps on tool calls per turn. They kept small, high-value facts: OS, repo status, and open or recently viewed files. Skip checklists for open-ended work. The model optimizes the listed items and deprioritizes everything else. ## 3. Tool definitions Tool schemas ride along on every request. Most tools beyond the core set were each needed in under 20% of conversations, and moving them out of static context cut tool-description tokens 60%. Doing the same for integration tools (such as MCP servers), with names in context and full schemas in one folder per server that the agent can search with grep or jq, cut total tokens 46.9% in sessions that used them. - Keep in static context: high-frequency tools (for a coding agent: read, search, edit, shell), tools the model tries to call even when they're absent, and tools a mode depends on. - Offload the rest: leave a name or one-line pointer and make the full schema discoverable on demand. Group related tools so they load together, and put status (such as "needs re-authentication") where the agent will see it. - Tighten what remains: describe behavior and arguments, and drop usage lectures. - Pick the split by testing a few configurations and tracking tokens, cost, latency, tool-call errors, and task success. ## 4. Cache layout Order each request so the reusable prefix is as long as possible: `tool definitions → system instructions → [breakpoint] → setup message (skills, subagents, rules, environment) → [breakpoint] → conversation` - Keep the prefix byte-identical across turns. Use deterministic tool order and serialization, put timestamps and IDs after the boundary, and don't rewrite earlier messages except when compacting. - Use explicit breakpoints if the provider supports them. Otherwise rely on automatic prefix caching with the stable part first. Respect TTL and minimum-length rules. - Switching models mid-conversation throws away the cache (caches are per model and provider) and hands the new model a history it didn't write. When a different model is needed, run it as a subagent with fresh context. Explicit breakpoints plus moving per-request setup after them cut cold cache misses 20%. ## 5. Tool results and other context added during a run - Large outputs (commands, integrations, logs): write them to a file and return the path, size, and a short tail. The agent can tail, grep, or read ranges for more. Truncating loses data, and inlining bloats every later request. Treat long-running terminal sessions the same way. - High-volume formats: look for overhead repeated on every line or item. Numbering every 10th line of a file read instead of every line cut cache-read tokens 1.6% without hurting citation accuracy. Each number costs 3–5 tokens, and agents read tens of thousands of lines per session. Also check repeated absolute paths, verbose JSON keys, ANSI codes, progress bars, and repeated headers. - Good retrieval saves exploration turns. Adding semantic search alongside grep raised codebase question-answering accuracy 12.5% on average and cut the iterations users needed. - Tool errors waste tokens and leave confusing debris in context. Classify expected errors (invalid arguments, unexpected environment, provider error, timeout, user abort), treat unknown errors as harness bugs, and track rates per tool and per model. One focused effort along these lines cut unexpected tool errors 10×. ## 6. Long runs: compaction, subagents, and model mix - Compaction: keep the summarization prompt short and the summary compact, carry forward plan state and remaining tasks, and save the full history to a file the agent can search for details the summary dropped. A model trained to self-summarize from a one-line prompt wrote ~1k-token summaries with half the compaction error of a multi-thousand-token prompt that produced 5k+ token summaries. Untrained models may need more guidance, so test how short you can go. A more expensive summarization model made a negligible difference. - Scratchpads and running notes: rewrite them instead of appending. For repeated work in one environment, a small agent-maintained notes file with a line budget, loaded at start, is a promising way to shorten later runs. - Subagents: fresh context keeps the parent lean, but isolation adds coordination cost (duplicate or stale work). If the model already delegates on its own, remove prompting that pushes it to. Have subagents return short handoffs: what was done, findings, concerns, and deviations. A subagent should use a different model only when the user or harness says so. - Model mix: in large multi-agent runs, workers used at least 69% of tokens, and over 90% in most runs. A frontier planner with cheap workers matched a frontier model doing everything at about one-eighth the cost. Planner choice still changes worker spend. One planner that cost less on its own saw its workers use several times more tokens, and the run cost more overall. Measure the whole tree. - Routing and reasoning effort: send simple turns to a cheaper model or lower effort, and upgrade only when a stronger model is clearly better. A router built this way matched or beat single frontier models on user satisfaction at 41–68% lower cost. - Reasoning continuity: if the API returns reasoning items (including encrypted ones), pass them back on later turns and alert when they go missing. Dropping them cost one reasoning model 30% on a coding benchmark, and it burned tokens reconstructing its plan. ## 7. Fit the harness to each model Adapt to what each model was trained on instead of forcing one shape on all of them. If you've tuned the harness for a similar model, start from that version. - Edit format: use the one the model was trained on (for example, patch-style or search-and-replace). An unfamiliar format costs extra reasoning tokens and causes more mistakes. - Shell or tools: shell-first models fall back to `cat` or inline scripts. Name tools after their shell equivalents (such as `rg`), and if needed add: "If a tool exists for an action, prefer to use the tool instead of shell commands (e.g. read_file over `cat`)." - Literalness: some model families follow instructions literally and others tolerate imprecision. Some spiral on emphasized wording. Strip caps and emphasis for literal models. - Triggers: some models ignore a tool until told when to use it. A literal trigger works: "After substantive edits, use the to check recently edited files for linter errors. If you've introduced any, fix them if you can easily figure out how." - Progress updates: if a model reports progress through reasoning summaries, keep them to 1–2 sentences that note new findings or a change of tactic, and remove instructions about messaging mid-turn. - Quirks worth a targeted line: hedging or refusing as context fills ("context anxiety"), declaring completion early, stopping to ask permission, and calling tools that don't exist. Tie each added instruction to the transcript behavior it fixes. Re-audit when models change, since guidance one version needed can be dead weight for the next. ## 8. Validate - Offline: run a fixed set of realistic tasks before and after, ideally drawn from real usage and phrased the way users actually write (short and ambiguous). Compare task success, tokens, cost per task, turns, and tool errors. Don't ship a change that lowers success. - Online, if you have users: A/B test each change or small bundle. The primary metric is cost per completed task. Guardrails are task success signals, tool-call errors, latency, turns per task, and cache hit rate. For a coding agent, a good success signal is how much agent-written code survives over time. In general, check whether the user's next message moves on or reports a problem. - Ship only when cost drops and no guardrail regresses beyond noise. Record null results. ## What to change directly and what to propose - Change directly, each in its own revertible commit: token and cache telemetry, deterministic serialization and tool order, moving volatile content out of the cached prefix, explicit cache breakpoints, writing large outputs to files instead of truncating, passing back reasoning items that are being dropped, and fixes for recurring tool errors. - Change behind a flag so it can be tested: system prompt edits, tool offloading, output format changes, compaction changes, and subagent prompting. - Propose only: changes to which models run, routing, reasoning-effort defaults, or how work is split across agents. ## Traps - Asking the model to use fewer tokens or do less. - Truncating tool output. - Dropping reasoning items to save input tokens. - Volatile content in the cached prefix, or tool order that changes between requests. - Offloading a tool the model needs on the first turn or tries to call when it's missing. - Emphasis-heavy prompts (MUST, NEVER, IMPORTANT, all caps), especially with literal models. - Forcing a terser output format than the model was trained on. Fewer output tokens can mean less thinking and worse results. - Optimizing raw token counts instead of cost, per request instead of per task, or evals instead of real usage. - Switching models mid-conversation to save money. - Adding coordination layers that become bottlenecks. ## Report back with 1. The harness map and baseline: cost by source × billing type, with the biggest sources called out. 2. A ranked list of changes: layer, what changes, estimated savings and how you estimated them, quality risk, how to validate, and how to roll back. 3. The changes you made, including a system prompt diff with a keep, rewrite, delete, or move reason for each line. 4. A test plan for the flagged changes. 5. Gaps: anything you couldn't find or measure.

Introducing Xiaomi MiMo-V2.6 — Pro & Flash. Frontier intelligence, all the modalities, built in public. 🔹 Two omnimodal models, advancing through scaled reinforcement learning 🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks 🔹 Pro scores 46 on the Artificial Analysis Intelligence Index — the highest among open-source models 🔹 Stronger coding, computer use, 3D reasoning and creative capabilities 🔹 Open model weights, technical report, RL environments and training code Blog：

According to Artificial Analysis, Ling-3.0-tiny sits on the mobile intelligence–speed Pareto frontier: 59 at 16K and 5.7s on iPhone 17 Pro. It also ranks first in the 64K intelligence evaluation with a score of 66. Bringing stronger intelligence to smaller devices.

BREAKING: 𝕏 has published the latest update to the open-source algorithm powering the For You feed. The new update changes 47 files, adding 5,850 lines and removing 586. Key changes: • Phoenix now uses a long-dwell setting instead of short dwell, placing more emphasis on time spent viewing posts • The ranking weight for opening a video rises from 0.05 to 0.07, while dwell rises from 0 to 0.05 • New indexes help retrieve immersive videos receiving likes across different age ranges, extending up to 30 days • The Following feed gets stronger block and mute protection, including quotes and reposts involving blocked accounts or accounts that blocked the viewer • Cold-start ranking now uses views received specifically on Home instead of total views. Its Top-K setting drops from 5 to 2 • The follower threshold separating reply-spam detection from special reply ranking moves from 100,000 to 120,000 • Abuse models can now request approved actions such as labels, suspensions and account challenges, protected by allowlists, logging and a 16-action safety cap • The existing Brazil 2026 election-filter list expands from 665 to 2,328 account IDs. Posts remain eligible for people explicitly following those accounts • X’s newer ad brand-safety check is now enabled by default • A new priority-post stream has been added to the early content-quality screening system 𝕏 is not just talking about algorithmic transparency. It is publishing the code. The most transparent platform on the internet.

Grok Summary of JPMorgan’s new note today on @SpaceX and @Grok • JPMorgan maintained its Overweight rating on SpaceX, saying it is “increasingly positive” about Grok. • The completed Cursor acquisition is an important step in expanding SpaceX’s enterprise AI capabilities. • JPMorgan estimates that Cursor generates roughly $4 billion in annual recurring revenue, with about 75% coming from business customers. Cursor also gives SpaceX an enterprise distribution channel and valuable coding data. • JPMorgan has already seen “tangible improvements” in recent Grok models after Cursor data was incorporated into their supplemental training. • The firm says Grok 4.6 combines frontier intelligence with meaningfully lower costs than its peers, supporting stronger adoption. • Grok Bot expands SpaceX into enterprise AI agents that can perform tasks across workplace applications, targeting the fast-growing AI productivity market. • JPMorgan expects monthly model releases throughout the rest of 2026, culminating with Grok 5 in December. The firm expects Grok 5 to deliver a major improvement in performance and reach. • JPMorgan believes Cursor’s coding expertise, combined with roughly 25 years of SpaceX’s proprietary engineering knowledge, could give Grok an advantage in software development and complex engineering applications. • The firm expects improving Grok monetization, particularly among enterprise customers, to become an increasingly important driver of SpaceX’s AI revenue. • JPMorgan expects Grok, Cursor and other enterprise AI products to become core long-term growth drivers alongside launch services and Starlink. SpaceX is steadily turning AI into a powerful long-term growth engine. Bullish.

My investigation on the GTA 6 Leaker is done and I have emailed all the evidence to TAKE2 and Rockstar Games legal team As much as I wanted and loved to actually reveal who we are dealing with here, full identity and all, I believe that would be doxxing. Since the way I got to this guy was through a mix of OSINT and some people familiar with the matter via TG, I don’t want anyone to get into trouble, so for now I’ll leave out personal details and how I obtained the information One of the stronger findings links to what appears to be this guy’s personal bank account, the bank is located in Europe. It’s obviously not out of the question that the bank account in question might be stolen, but given the circumstances and some other correlations I saw, I believe we have him Going forward, I’ll refer to him as Leeker With the data I have provided to T2, I believe it will make their job easier when cooperating with law enforcement and help pin down the people involved (it’s not just one). Given the nature of the data I found, revealing it publicly could give the guys time to cover their tracks or alter evidence But I don’t want to leave people hanging so I’ll share some stuff below and answer as much as I can in the comments without it being damaging Stuff I have learned that can be shared: -> The leeker is actually a threat actor who has previous malicious activity under a different alias It also seems that there is genuine hatred toward this guy from the people around him. Much of my current information comes from his peers snitching after my first post -> This leak was apparently due to a breach or an insider. Nature of how is still unknown to me, But I’ve received two different stories about this so I'll share them anyway: 1. One story I got is that leeker bought a backdoor access or some sort from someone 2. Another person told me this is somewhat related to some incident inside Rockstar a while ago Keep in mind it’s pretty common for these crypto bros to boast and lie about their “gains” to each other, so I honestly don’t know which to believe here -> Not related at all to the ‘Mafia 808’ group -> I haven’t heard anything about Rockstar India being hacked unlike some other people are claiming -> This build we are seeing here is apparently from a year ago (as confirmed in ResetEra forums), but the theft I believe happened in April 2026 and the leeker has been preparing since then -> From what I can tell, and as I explained in one of the earlier posts, I don’t think they possess a live build. Most likely pre-recorded footage, but this is just my assumption The voting thing they did initially was to make people want to get the coin so the market cap can grow I highly recommend staying away from the coin. This is a literal pump and dump, don't buy into their "Fighting for gamers" bullshit. I won’t be surprised if they at some point start threatening to leak the story unless people donate to them in the poll. Even if that happens, don’t do it That’s all I can responsibly share right now. Questions that don’t risk the investigation or the people involved are welcome in the replies

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